Systems and methods for detecting arrhythmia from a physiological signal

ABSTRACT

Arrhythmia may impact the determination of physiological information from a physiological signal. A patient monitoring system may detect the presence of arrhythmia based on changes in the physiological signal. Derived value data sets may be extracted from the physiological signal and calculations performed to generate arrhythmia features. The arrhythmia features may be used to generate an arrhythmia indicator that may indicate the presence of arrhythmia in the physiological signal.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. application Ser. No.13/307,927 (Publication No. US-2013-0138002), filed Nov. 30, 2011, theentire contents of which are incorporated herein by reference.

SUMMARY

The present disclosure relates to physiological signal processing, andmore particularly relates to detecting arrhythmia from a physiologicalsignal.

A patient monitoring system may be configured to determine physiologicalinformation such as respiration information from a physiological signalsuch as a photoplethysmograph (PPG) signal. For example, a PPG signalmay exhibit amplitude and frequency modulation based on the respirationof a patient. Arrhythmias may also impact a physiological signal such asa PPG signal, and in some instances may obscure the determination of thedesired physiological information.

A patient monitoring system may receive a physiological signal such as aPPG signal. Derived value data sets that are indicative of arrhythmiamay be extracted from the physiological signal and analyzed to determinearrhythmia features. The arrhythmia features may be combined andcompared to one or more thresholds to generate an arrhythmia indicator.The arrhythmia indicator may indicate that an arrhythmia has beendetected, e.g., by generating a confidence value. The patient monitoringsystem may utilize the confidence value for further processing of thephysiological information.

BRIEF DESCRIPTION OF THE FIGURES

The above and other features of the present disclosure, its nature andvarious advantages will be more apparent upon consideration of thefollowing detailed description, taken in conjunction with theaccompanying drawings in which:

FIG. 1 shows an illustrative patient monitoring system in accordancewith some embodiments of the present disclosure;

FIG. 2 is a block diagram of the illustrative patient monitoring systemof FIG. 1 coupled to a patient in accordance with some embodiments ofthe present disclosure;

FIG. 3 shows a block diagram of an illustrative signal processing systemin accordance with some embodiments of the present disclosure;

FIG. 4 shows an illustrative PPG signal that may be analyzed inaccordance with some embodiments of the present disclosure;

FIG. 5 shows an illustrative PPG signal having morphologycharacteristics relating to respiration in accordance with someembodiments of the present disclosure;

FIG. 6 is a flow diagram showing illustrative steps for detectingarrhythmia in accordance with some embodiments of the presentdisclosure;

FIG. 7A is a flow diagram showing illustrative steps for determiningexemplary arrhythmia features in accordance with some embodiments of thepresent disclosure;

FIG. 7B is a flow diagram showing illustrative steps for determining anexemplary arrhythmia indicator in accordance with some embodiments ofthe present disclosure;

FIG. 8 is a flow diagram showing illustrative steps for generating a FMdemodulated derived value data set in accordance with some embodimentsof the present disclosure; and

FIG. 9 shows an illustrative PPG signal and FM demodulated derived valuedata set in accordance with some embodiments of the present disclosure.

DETAILED DESCRIPTION OF THE FIGURES

The present disclosure is directed towards detecting arrhythmia from aphysiological signal. A patient monitoring system may receive one ormore physiological signals, such as a photoplethysmograph (PPG) signalgenerated by a pulse oximeter sensor coupled to a patient. The patientmonitoring system may extract physiological and morphology derived valuedata sets from the physiological signal such as pulse rate data set, akurtosis derived value data set, and a b/a ratio derived value data set.

The derived value data sets may be utilized to calculate arrhythmiafeatures. Arrhythmia features may include, for example, a standarddeviation feature based on the pulse rate data set, an entropy featurecalculated from the pulse rate data set, a dot product featurecalculated from the kurtosis data set and the b/a ratio data set, anyother suitable derived value data set, or any combination thereof. Arelationship between the arrhythmia features and whether arrhythmia ispresent may be determined based on a learning algorithm. Suitablecalculations may be performed on the arrhythmia features to generate anarrhythmia indicator. In some embodiments, the arrhythmia indicator maybe based on the arrhythmia features, a set of weighting valuesassociated with the arrhythmia features, and a bias value. In anexemplary embodiment, these values may be input to a trained neural net.

The arrhythmia indicator may be generated by decision logic which maydetect arrhythmia. In some embodiments, the arrhythmia indicator may bea confidence value that varies between a “non-arrhythmia” value(e.g., 1) and an “arrhythmia” value (e.g., 0). Confidence values fallingbetween 0 and 1 may indicate a relative likelihood of the presence ofarrhythmia. In an exemplary embodiment, the decision logic may beimplemented with fuzzy logic.

In some embodiments, a confidence value between 0 and 1 may indicate therelative likelihood that a particular type of arrhythmia is present (andtherefore more or less likely to impact the calculation of a particularphysiological parameter such as respiration rate). In an exemplaryembodiment, some arrhythmias (e.g., respiratory sinus arrhythmia orisolated premature ventricular contraction) may result in a confidencevalue closer to 1 (i.e., non-arrhythmia) while others (e.g., atrialfibrillation or frequent premature ventricular contraction) may resultmay result in a confidence value closer to 0 (i.e., arrhythmia).

For purposes of clarity, the present disclosure is written in thecontext of the physiological signal being a PPG signal generated by apulse oximetry system. It will be understood that any other suitablephysiological signal or any other suitable system may be used inaccordance with the teachings of the present disclosure.

An oximeter is a medical device that may determine the oxygen saturationof the blood. One common type of oximeter is a pulse oximeter, which mayindirectly measure the oxygen saturation of a patient's blood (asopposed to measuring oxygen saturation directly by analyzing a bloodsample taken from the patient). Pulse oximeters may be included inpatient monitoring systems that measure and display various blood flowcharacteristics including, but not limited to, the oxygen saturation ofhemoglobin in arterial blood. Such patient monitoring systems may alsomeasure and display additional physiological parameters, such as apatient's pulse rate.

An oximeter may include a light sensor that is placed at a site on apatient, typically a fingertip, toe, forehead or earlobe, or in the caseof a neonate, across a foot. The oximeter may use a light source to passlight through blood perfused tissue and photoelectrically sense theabsorption of the light in the tissue. In addition, locations that arenot typically understood to be optimal for pulse oximetry serve assuitable sensor locations for the monitoring processes described herein,including any location on the body that has a strong pulsatile arterialflow. For example, additional suitable sensor locations include, withoutlimitation, the neck to monitor carotid artery pulsatile flow, the wristto monitor radial artery pulsatile flow, the inside of a patient's thighto monitor femoral artery pulsatile flow, the ankle to monitor tibialartery pulsatile flow, and around or in front of the ear. Suitablesensors for these locations may include sensors for sensing absorbedlight based on detecting reflected light. In all suitable locations, forexample, the oximeter may measure the intensity of light that isreceived at the light sensor as a function of time. The oximeter mayalso include sensors at multiple locations. A signal representing lightintensity versus time or a mathematical manipulation of this signal(e.g., a scaled version thereof, a log taken thereof, a scaled versionof a log taken thereof, etc.) may be referred to as thephotoplethysmograph (PPG) signal. In addition, the term “PPG signal,” asused herein, may also refer to an absorption signal (i.e., representingthe amount of light absorbed by the tissue) or any suitable mathematicalmanipulation thereof. The light intensity or the amount of lightabsorbed may then be used to calculate any of a number of physiologicalparameters, including an amount of a blood constituent (e.g.,oxyhemoglobin) being measured as well as a pulse rate and when eachindividual pulse occurs.

In some applications, the light passed through the tissue is selected tobe of one or more wavelengths that are absorbed by the blood in anamount representative of the amount of the blood constituent present inthe blood. The amount of light passed through the tissue varies inaccordance with the changing amount of blood constituent in the tissueand the related light absorption. Red and infrared (IR) wavelengths maybe used because it has been observed that highly oxygenated blood willabsorb relatively less Red light and more IR light than blood with alower oxygen saturation. By comparing the intensities of two wavelengthsat different points in the pulse cycle, it is possible to estimate theblood oxygen saturation of hemoglobin in arterial blood.

When the measured blood parameter is the oxygen saturation ofhemoglobin, a convenient starting point assumes a saturation calculationbased at least in part on Lambert-Beer's law. The following notationwill be used herein:

I(λ,t)=I _(o)(λ)exp(−(sβ _(o)(λ)+(1−s)β_(r)(λ))l(t)  (1)

where:λ=wavelength;t=time;I=intensity of light detected;I₀=intensity of light transmitted;s=oxygen saturation;β₀,β₀=empirically derived absorption coefficients; andl(t)=a combination of concentration and path length from emitter todetector as a function of time.

The traditional approach measures light absorption at two wavelengths(e.g., Red and IR), and then calculates saturation by solving for the“ratio of ratios” as follows:

1. The natural logarithm of Eq. 1 is taken (“log” will be used torepresent the natural logarithm) for IR and Red to yield

log I=log I _(o)−(sβ _(o)+(1−s)β_(r))l.  (2)

2. Eq. 2 is then differentiated with respect to time to yield

$\begin{matrix}{\frac{{\log}\; I}{t} = {{- \left( {{s\; \beta_{o}} + {\left( {1 - s} \right)\beta_{r}}} \right)}{\frac{l}{t}.}}} & (3)\end{matrix}$

3. Eq. 3, evaluated at the Red wavelength λ_(R), is divided by Eq. 3evaluated at the IR wavelength λ_(IR), in accordance with

$\begin{matrix}{\frac{{\log}\; {{I\left( \lambda_{R} \right)}/{t}}}{{\log}\; {{I\left( \lambda_{I\; R} \right)}/{t}}} = {\frac{{s\; {\beta_{o}\left( \lambda_{R} \right)}} + {\left( {1 - s} \right){\beta_{r}\left( \lambda_{R} \right)}}}{{s\; {\beta_{o}\left( \lambda_{I\; R} \right)}} + {\left( {1 - s} \right){\beta_{r}\left( \lambda_{I\; R} \right)}}}.}} & (4)\end{matrix}$

4. Solving for s yields

$\begin{matrix}{s = {\frac{{\frac{{\log}\; {I\left( \lambda_{I\; R} \right)}}{t}{\beta_{r}\left( \lambda_{R} \right)}} - {\frac{{\log}\; {I\left( \lambda_{R} \right)}}{t}{\beta_{r}\left( \lambda_{{I\; R}\;} \right)}}}{\begin{matrix}{{\frac{{\log}\; {I\left( \lambda_{R} \right)}}{t}\left( {{\beta_{o}\left( \lambda_{I\; R} \right)} - {\beta_{r}\left( \lambda_{I\; R} \right)}} \right)} -} \\{\frac{{\log}\; {I\left( \lambda_{I\; R} \right)}}{t}\left( {{\beta_{o}\left( \lambda_{R} \right)} - {\beta_{r}\left( \lambda_{R} \right)}} \right)}\end{matrix}}.}} & (5)\end{matrix}$

5. Note that, in discrete time, the following approximation can be made:

$\begin{matrix}{\frac{{\log}\; {I\left( {\lambda,t} \right)}}{t} \simeq {{\log \; {I\left( {\lambda,t_{2}} \right)}} - {\log \; {{I\left( {\lambda,t_{1}} \right)}.}}}} & (6)\end{matrix}$

6. Rewriting Eq. 6 by observing that log A−log B=log(A/B) yields

$\begin{matrix}{\frac{{\log}\; {I\left( {\lambda,t} \right)}}{t} \simeq {{\log \left( \frac{I\left( {t_{2},\lambda} \right)}{I\left( {t_{1},\lambda} \right)} \right)}.}} & (7)\end{matrix}$

7. Thus, Eq. 4 can be expressed as

$\begin{matrix}{{{\frac{\frac{{\log}\; {I\left( \lambda_{R} \right)}}{t}}{\frac{{\log}\; {I\left( \lambda_{I\; R} \right)}}{t}} \simeq \frac{\log \left( \frac{I\left( {t_{1},\lambda_{R}} \right)}{I\left( {t_{2},\lambda_{R}} \right)} \right)}{\log \left( \frac{I\left( {t_{1},\lambda_{I\; R}} \right)}{I\left( {t_{2},\lambda_{I\; R}} \right)} \right)}} = R},} & (8)\end{matrix}$

where R represents the “ratio of ratios.”8. Solving Eq. 4 for s using the relationship of Eq. 5 yields

$\begin{matrix}{s = {\frac{{\beta_{r}\left( \lambda_{R} \right)} - {R\; {\beta_{r}\left( \lambda_{I\; R} \right)}}}{{R\left( {{\beta_{o}\left( \lambda_{I\; R} \right)} - {\beta_{r}\left( \lambda_{I\; R} \right)}} \right)} - {\beta_{o}\left( \lambda_{R} \right)} + {\beta_{r}\left( \lambda_{R} \right)}}.}} & (9)\end{matrix}$

9. From Eq. 8, R can be calculated using two points (e.g., PPG maximumand minimum), or a family of points. One method applies a family ofpoints to a modified version of Eq. 8. Using the relationship

$\begin{matrix}{{\frac{{\log}\; I}{t} = \frac{{I}/{t}}{I}},} & (10)\end{matrix}$

Eq. 8 becomes

$\begin{matrix}\begin{matrix}{\frac{\frac{{\log}\; {I\left( \lambda_{R} \right)}}{t}}{\frac{{\log}\; {I\left( \lambda_{I\; R} \right)}}{t}} \simeq \frac{\frac{{I\left( {t_{2},\lambda_{R}} \right)} - {I\left( {t_{1},\lambda_{R}} \right)}}{I\left( {t_{1},\lambda_{R}} \right)}}{\frac{{I\left( {t_{2},\lambda_{I\; R}} \right)} - {I\left( {t_{1},\lambda_{I\; R}} \right)}}{I\left( {t_{1},\lambda_{I\; R}} \right)}}} \\{= \frac{\left\lbrack {{I\left( {t_{2},\lambda_{R}} \right)} - {I\left( {t_{1},\lambda_{R}} \right)}} \right\rbrack {I\left( {t_{1},\lambda_{I\; R}} \right)}}{\left\lbrack {{I\left( {t_{2},\lambda_{I\; R}} \right)} - {I\left( {t_{1},\lambda_{I\; R}} \right)}} \right\rbrack {I\left( {t_{1},\lambda_{R}} \right)}}} \\{{= R},}\end{matrix} & (11)\end{matrix}$

which defines a cluster of points whose slope of y versus x will give Rwhen

x=[I(t ₂,λ_(IR))−I(t ₁,λ_(IR))]I(t ₁,λ_(R)),  (12)

and

y=[I(t ₂,λ_(R))−I(t ₁,λ_(R))]I(t ₁,λ_(IR)).  (13)

Once R is determined or estimated, for example, using the techniquesdescribed above, the blood oxygen saturation can be determined orestimated using any suitable technique for relating a blood oxygensaturation value to R. For example, blood oxygen saturation can bedetermined from empirical data that may be indexed by values of R,and/or it may be determined from curve fitting and/or otherinterpolative techniques.

FIG. 1 is a perspective view of an embodiment of a patient monitoringsystem 10. System 10 may include sensor unit 12 and monitor 14. In someembodiments, sensor unit 12 may be part of an oximeter. Sensor unit 12may include an emitter 16 for emitting light at one or more wavelengthsinto a patient's tissue. A detector 18 may also be provided in sensorunit 12 for detecting the light originally from emitter 16 that emanatesfrom the patient's tissue after passing through the tissue. Any suitablephysical configuration of emitter 16 and detector 18 may be used. In anembodiment, sensor unit 12 may include multiple emitters and/ordetectors, which may be spaced apart. System 10 may also include one ormore additional sensor units (not shown) that may take the form of anyof the embodiments described herein with reference to sensor unit 12. Anadditional sensor unit may be the same type of sensor unit as sensorunit 12, or a different sensor unit type than sensor unit 12. Multiplesensor units may be capable of being positioned at two differentlocations on a subject's body; for example, a first sensor unit may bepositioned on a patient's forehead, while a second sensor unit may bepositioned at a patient's fingertip.

Sensor units may each detect any signal that carries information about apatient's physiological state, such as an electrocardiograph signal,arterial line measurements, or the pulsatile force exerted on the wallsof an artery using, for example, oscillometric methods with apiezoelectric transducer. According to another embodiment, system 10 mayinclude two or more sensors forming a sensor array in lieu of either orboth of the sensor units. Each of the sensors of a sensor array may be acomplementary metal oxide semiconductor (CMOS) sensor. Alternatively,each sensor of an array may be charged coupled device (CCD) sensor. Insome embodiments, a sensor array may be made up of a combination of CMOSand CCD sensors. The CCD sensor may comprise a photoactive region and atransmission region for receiving and transmitting data whereas the CMOSsensor may be made up of an integrated circuit having an array of pixelsensors. Each pixel may have a photodetector and an active amplifier. Itwill be understood that any type of sensor, including any type ofphysiological sensor, may be used in one or more sensor units inaccordance with the systems and techniques disclosed herein. It isunderstood that any number of sensors measuring any number ofphysiological signals may be used to determine physiological informationin accordance with the techniques described herein.

In some embodiments, emitter 16 and detector 18 may be on opposite sidesof a digit such as a finger or toe, in which case the light that isemanating from the tissue has passed completely through the digit. Insome embodiments, emitter 16 and detector 18 may be arranged so thatlight from emitter 16 penetrates the tissue and is reflected by thetissue into detector 18, such as in a sensor designed to obtain pulseoximetry data from a patient's forehead.

In some embodiments, sensor unit 12 may be connected to and draw itspower from monitor 14 as shown. In another embodiment, the sensor may bewirelessly connected to monitor 14 and include its own battery orsimilar power supply (not shown). Monitor 14 may be configured tocalculate physiological parameters (e.g., pulse rate, blood oxygensaturation, and respiration information) based at least in part on datarelating to light emission and detection received from one or moresensor units such as sensor unit 12 and an additional sensor (notshown). In some embodiments, the calculations may be performed on thesensor units or an intermediate device and the result of thecalculations may be passed to monitor 14. Further, monitor 14 mayinclude a display 20 configured to display the physiological parametersor other information about the system. In the embodiment shown, monitor14 may also include a speaker 22 to provide an audible sound that may beused in various other embodiments, such as for example, sounding anaudible alarm in the event that a patient's physiological parameters arenot within a predefined normal range. In some embodiments, the system 10includes a stand-alone monitor in communication with the monitor 14 viaa cable or a wireless network link.

In some embodiments, sensor unit 12 may be communicatively coupled tomonitor 14 via a cable 24. In some embodiments, a wireless transmissiondevice (not shown) or the like may be used instead of or in addition tocable 24. Monitor 14 may include a sensor interface configured toreceive physiological signals from sensor unit 12, provide signals andpower to sensor unit 12, or otherwise communicate with sensor unit 12.The sensor interface may include any suitable hardware, software, orboth, which may be allow communication between monitor 14 and sensorunit 12.

Patient monitoring system 10 may also include display monitor 26.Monitor 14 may be in communication with display monitor 26. Displaymonitor 26 may be any electronic device that is capable of communicatingwith monitor 14 and calculating and/or displaying physiologicalparameters, e.g., a general purpose computer, tablet computer, smartphone, or an application-specific device. Display monitor 26 may includea display 28 and user interface 30. Display 28 may include touch screenfunctionality to allow a user to interface with display monitor 26 bytouching display 28 and utilizing motions. User interface 30 may be anyinterface that allows a user to interact with display monitor 26, e.g.,a keyboard, one or more buttons, a camera, or a touchpad.

Monitor 14 and display monitor 26 may communicate utilizing any suitabletransmission medium, including wireless (e.g., WiFi, Bluetooth, etc.),wired (e.g., USB, Ethernet, etc.), or application-specific connections.In an exemplary embodiment, monitor 14 and display monitor 26 may beconnected via cable 32. Monitor 14 and display monitor 26 maycommunicate utilizing standard or proprietary communications protocols,such as the Standard Host Interface Protocol (SHIP) developed and usedby Covidien of Mansfield, Mass. In addition, monitor 14, display monitor26, or both may be coupled to a network to enable the sharing ofinformation with servers or other workstations (not shown). Monitor 14,display monitor 26, or both may be powered by a battery (not shown) orby a conventional power source such as a wall outlet.

Monitor 14 may transmit calculated physiological parameters (e.g., pulserate, blood oxygen saturation, and respiration information) to displaymonitor 26. In some embodiments, monitor 14 may transmit a PPG signal,data representing a PPG signal, or both to display monitor 26, such thatsome or all calculated physiological parameters (e.g., pulse rate, bloodoxygen saturation, and respiration information) may be calculated atdisplay monitor 26. In an exemplary embodiment, monitor 14 may calculatepulse rate and blood oxygen saturation, while display monitor 26 maycalculate respiration information such as a respiration rate.

FIG. 2 is a block diagram of a patient monitoring system, such aspatient monitoring system 10 of FIG. 1, which may be coupled to apatient 40 in accordance with an embodiment. Certain illustrativecomponents of sensor unit 12 and monitor 14 are illustrated in FIG. 2.

Sensor unit 12 may include emitter 16, detector 18, and encoder 42. Inthe embodiment shown, emitter 16 may be configured to emit at least twowavelengths of light (e.g., Red and IR) into a patient's tissue 40.Hence, emitter 16 may include a Red light emitting light source such asRed light emitting diode (LED) 44 and an IR light emitting light sourcesuch as IR LED 46 for emitting light into the patient's tissue 40 at thewavelengths used to calculate the patient's physiological parameters. Insome embodiments, the Red wavelength may be between about 600 nm andabout 700 nm, and the IR wavelength may be between about 800 nm andabout 1000 nm. In embodiments where a sensor array is used in place of asingle sensor, each sensor may be configured to emit a singlewavelength. For example, a first sensor may emit only a Red light whilea second sensor may emit only an IR light. In a further example, thewavelengths of light used may be selected based on the specific locationof the sensor.

It will be understood that, as used herein, the term “light” may referto energy produced by radiation sources and may include one or more ofradio, microwave, millimeter wave, infrared, visible, ultraviolet, gammaray or X-ray electromagnetic radiation. As used herein, light may alsoinclude electromagnetic radiation having any wavelength within theradio, microwave, infrared, visible, ultraviolet, or X-ray spectra, andthat any suitable wavelength of electromagnetic radiation may beappropriate for use with the present techniques. Detector 18 may bechosen to be specifically sensitive to the chosen targeted energyspectrum of the emitter 16.

In some embodiments, detector 18 may be configured to detect theintensity of light at the Red and IR wavelengths. Alternatively, eachsensor in the array may be configured to detect an intensity of a singlewavelength. In operation, light may enter detector 18 after passingthrough the patient's tissue 40. Detector 18 may convert the intensityof the received light into an electrical signal. The light intensity isdirectly related to the absorbance and/or reflectance of light in thetissue 40. That is, when more light at a certain wavelength is absorbedor reflected, less light of that wavelength is received from the tissueby the detector 18. After converting the received light to an electricalsignal, detector 18 may send the signal to monitor 14, wherephysiological parameters may be calculated based on the absorption ofthe Red and IR wavelengths in the patient's tissue 40.

In some embodiments, encoder 42 may contain information about sensorunit 12, such as what type of sensor it is (e.g., whether the sensor isintended for placement on a forehead or digit) and the wavelengths oflight emitted by emitter 16. This information may be used by monitor 14to select appropriate algorithms, lookup tables and/or calibrationcoefficients stored in monitor 14 for calculating the patient'sphysiological parameters.

Encoder 42 may contain information specific to patient 40, such as, forexample, the patient's age, weight, and diagnosis. This informationabout a patient's characteristics may allow monitor 14 to determine, forexample, patient-specific threshold ranges in which the patient'sphysiological parameter measurements should fall and to enable ordisable additional physiological parameter algorithms. This informationmay also be used to select and provide coefficients for equations fromwhich measurements may be determined based at least in part on thesignal or signals received at sensor unit 12. For example, some pulseoximetry sensors rely on equations to relate an area under a portion ofa PPG signal corresponding to a physiological pulse to determine bloodpressure. These equations may contain coefficients that depend upon apatient's physiological characteristics as stored in encoder 42. Encoder42 may, for instance, be a coded resistor that stores valuescorresponding to the type of sensor unit 12 or the type of each sensorin the sensor array, the wavelengths of light emitted by emitter 16 oneach sensor of the sensor array, and/or the patient's characteristics.In some embodiments, encoder 42 may include a memory on which one ormore of the following information may be stored for communication tomonitor 14: the type of the sensor unit 12; the wavelengths of lightemitted by emitter 16; the particular wavelength each sensor in thesensor array is monitoring; a signal threshold for each sensor in thesensor array; any other suitable information; or any combinationthereof.

In some embodiments, signals from detector 18 and encoder 42 may betransmitted to monitor 14. In the embodiment shown, monitor 14 mayinclude a general-purpose microprocessor 48 connected to an internal bus50. Microprocessor 48 may be adapted to execute software, which mayinclude an operating system and one or more applications, as part ofperforming the functions described herein. Also connected to bus 50 maybe a read-only memory (ROM) 52, a random access memory (RAM) 54, userinputs 56, display 20, data output 84, and speaker 22.

RAM 54 and ROM 52 are illustrated by way of example, and not limitation.Any suitable computer-readable media may be used in the system for datastorage. Computer-readable media are capable of storing information thatcan be interpreted by microprocessor 48. This information may be data ormay take the form of computer-executable instructions, such as softwareapplications, that cause the microprocessor to perform certain functionsand/or computer-implemented methods. Depending on the embodiment, suchcomputer-readable media may include computer storage media andcommunication media. Computer storage media may include volatile andnon-volatile, removable and non-removable media implemented in anymethod or technology for storage of information such ascomputer-readable instructions, data structures, program modules orother data. Computer storage media may include, but is not limited to,RAM, ROM, EPROM, EEPROM, flash memory or other solid state memorytechnology, CD-ROM, DVD, or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium that can be used to store the desired informationand that can be accessed by components of the system.

In the embodiment shown, a time processing unit (TPU) 58 may providetiming control signals to light drive circuitry 60, which may controlwhen emitter 16 is illuminated and multiplexed timing for Red LED 44 andIR LED 46. TPU 58 may also control the gating-in of signals fromdetector 18 through amplifier 62 and switching circuit 64. These signalsare sampled at the proper time, depending upon which light source isilluminated. The received signal from detector 18 may be passed throughamplifier 66, low pass filter 68, and analog-to-digital converter 70.The digital data may then be stored in a queued serial module (QSM) 72(or buffer) for later downloading to RAM 54 as QSM 72 is filled. In someembodiments, there may be multiple separate parallel paths havingcomponents equivalent to amplifier 66, filter 68, and/or A/D converter70 for multiple light wavelengths or spectra received. Any suitablecombination of components (e.g., microprocessor 48, RAM 54, analog todigital converter 70, any other suitable component shown or not shown inFIG. 2) coupled by bus 50 or otherwise coupled (e.g., via an externalbus), may be referred to as “processing equipment.”

In some embodiments, microprocessor 48 may determine the patient'sphysiological parameters, such as SpO₂, pulse rate, and/or respirationinformation, using various algorithms and/or look-up tables based on thevalue of the received signals and/or data corresponding to the lightreceived by detector 18. Signals corresponding to information aboutpatient 40, and particularly about the intensity of light emanating froma patient's tissue over time, may be transmitted from encoder 42 todecoder 74. These signals may include, for example, encoded informationrelating to patient characteristics. Decoder 74 may translate thesesignals to enable the microprocessor to determine the thresholds basedat least in part on algorithms or look-up tables stored in ROM 52. Insome embodiments, user inputs 56 may be used to enter information,select one or more options, provide a response, input settings, anyother suitable inputting function, or any combination thereof. Userinputs 56 may be used to enter information about the patient, such asage, weight, height, diagnosis, medications, treatments, and so forth.In some embodiments, display 20 may exhibit a list of values, which maygenerally apply to the patient, such as, for example, age ranges ormedication families, which the user may select using user inputs 56.

Calibration device 80, which may be powered by monitor 14 via acommunicative coupling 82, a battery, or by a conventional power sourcesuch as a wall outlet, may include any suitable signal calibrationdevice. Calibration device 80 may be communicatively coupled to monitor14 via communicative coupling 82, and/or may communicate wirelessly (notshown). In some embodiments, calibration device 80 is completelyintegrated within monitor 14. In some embodiments, calibration device 80may include a manual input device (not shown) used by an operator tomanually input reference signal measurements obtained from some othersource (e.g., an external invasive or non-invasive physiologicalmeasurement system).

Data output 84 may provide for communications with other devices such asdisplay monitor 26 utilizing any suitable transmission medium, includingwireless (e.g., WiFi, Bluetooth, etc.), wired (e.g., USB, Ethernet,etc.), or application-specific connections. Data output 84 may receivemessages to be transmitted from microprocessor 48 via bus 50. Exemplarymessages to be sent in an embodiment described herein may include PPGsignals to be transmitted to display monitor module 26.

The optical signal attenuated by the tissue of patient 40 can bedegraded by noise, among other sources. One source of noise is ambientlight that reaches the light detector. Another source of noise iselectromagnetic coupling from other electronic instruments. Movement ofthe patient also introduces noise and affects the signal. For example,the contact between the detector and the skin, or the emitter and theskin, can be temporarily disrupted when movement causes either to moveaway from the skin. Also, because blood is a fluid, it respondsdifferently than the surrounding tissue to inertial effects, which mayresult in momentary changes in volume at the point to which the oximeterprobe is attached.

Noise (e.g., from patient movement) can degrade a sensor signal reliedupon by a care provider, without the care provider's awareness. This isespecially true if the monitoring of the patient is remote, the motionis too small to be observed, or the care provider is watching theinstrument or other parts of the patient, and not the sensor site.Processing sensor signals (e.g., PPG signals) may involve operationsthat reduce the amount of noise present in the signals, control theamount of noise present in the signal, or otherwise identify noisecomponents in order to prevent them from affecting measurements ofphysiological parameters derived from the sensor signals.

FIG. 3 is an illustrative processing system 300 in accordance with anembodiment that may implement the signal processing techniques describedherein. In some embodiments, processing system 300 may be included in apatient monitoring system (e.g., patient monitoring system 10 of FIGS.1-2). Processing system 300 may include input signal 310, pre-processor312, processor 314, post-processor 316, and output 318. Pre-processor312, processor 314, and post-processor 316 may be any suitable software,firmware, hardware, or combination thereof for calculating physiologicalparameters such as respiration information based on input signal 310.For example, pre-processor 312, processor 314, and post-processor 316may include one or more hardware processors (e.g., integrated circuits),one or more software modules, computer-readable media such as memory,firmware, or any combination thereof. Pre-processor 312, processor 314,and post-processor 316 may, for example, be a computer or may be one ormore chips (i.e., integrated circuits). Pre-processor 312, processor314, and post-processor 316 may, for example, include an assembly ofanalog electronic components.

In some embodiments, processing system 300 may be included in monitor 14and/or display monitor 26 of a patient monitoring system (e.g., patientmonitoring system 10 of FIGS. 1-2). In the illustrated embodiment, inputsignal 310 may be a PPG signal. Input signal 310 may be a PPG signalthat was sampled and generated at monitor 14, for example at 76 Hz.Input signal 310, pre-processor 312, processor 314, and post-processor316 may reside entirely within a single device (e.g., monitor 14 ordisplay monitor 26) or may reside in multiple devices (e.g., monitor 14and display monitor 26).

Input signal 310 may be coupled to pre-processor 312. In someembodiments, input signal 310 may include PPG signals corresponding toone or more light frequencies, such as a Red PPG signal and an IR PPGsignal. In some embodiments, the signal may include signals measured atone or more sites on a patient's body, for example, a patient's finger,toe, ear, arm, or any other body site. In some embodiments, signal 310may include multiple types of signals (e.g., one or more of an ECGsignal, an EEG signal, an acoustic signal, an optical signal, a signalrepresenting a blood pressure, and a signal representing a heart rate).The signal may be any suitable biosignal or signals, such as, forexample, electrocardiogram, electroencephalogram, electrogastrogram,electromyogram, heart rate signals, pathological sounds, ultrasound, orany other suitable biosignal. The systems and techniques describedherein are also applicable to any dynamic signals, non-destructivetesting signals, condition monitoring signals, fluid signals,geophysical signals, astronomical signals, electrical signals, financialsignals including financial indices, sound and speech signals, chemicalsignals, meteorological signals including climate signals, any othersuitable signal, and/or any combination thereof.

Pre-processor 312 may be implemented by any suitable combination ofhardware and software. In an embodiment, pre-processor 312 may be anysuitable signal processing device and the signal received from inputsignal 310 may include one or more PPG signals. An exemplary receivedPPG signal may be received in a streaming fashion, or may be received ona periodic basis as a sampling window, e.g., every 5 seconds. Thereceived signal may include the PPG signal as well as other informationrelated to the PPG signal, e.g., a pulse found indicator, the mean pulserate from the PPG signal, the most recent pulse rate, an indicator forthe most recent invalid sample, and an indicator of the last artifactfor the PPG signal. It will be understood that input signal 310 mayinclude any suitable signal source, signal generating data, signalgenerating equipment, or any combination thereof to be provided topre-processor 312. The signal received at input signal 310 may be asingle signal, or may be multiple signals transmitted over a singlepathway or multiple pathways.

Pre-processor 312 may apply one or more signal processing operations toinput signal 310. For example, pre-processor 312 may apply apre-determined set of processing operations to input signal 310 toproduce a signal that may be appropriately analyzed and interpreted byprocessor 314, post-processor 316, or both. Pre-processor 312 mayperform any necessary operations to provide a signal that may be used asan input for processor 314 and post-processor 316 to determinephysiological information such as respiration information. Examplesinclude reshaping the signal for transmission, multiplexing the signal,modulating the signal onto carrier signals, compressing the signal,encoding the signal, filtering the signal, low-pass filtering, band-passfiltering, signal interpolation, downsampling of a signal, attenuatingthe signal, adaptive filtering, closed-loop filtering, any othersuitable filtering, and/or any combination thereof.

Other signal processing operations may be performed by pre-processor 312and may be related to producing morphology metrics suitable as inputs todetermine physiological information. Pre-processor 312 may performcalculations based on an analysis window of a series of recentlyreceived PPG signal sampling windows, e.g., a 45-second analysis windowmay correspond to the 9 most recent 5-second sampling windows. Thephysiological information may be respiration information, which mayinclude any information relating to respiration, e.g., respiration rate,change in respiration rate, breathing intensity, etc.

Because respiration has an impact on pulse characteristics, it may bepossible to determine respiration information from a PPG signal.However, other physiological phenomena such as certain arrhythmias mayalso have an impact on pulse characteristics. Exemplary arrhythmia typesthat may impact pulse characteristics include respiratory sinusarrhythmia (“RSA”), atrial fibrillation (“AF”), and prematureventricular contraction (“PVC”). In some instances the impact ofarrhythmia on the PPG signal may make it difficult to determine adesired physiological parameter such as respiration information from thePPG signal. The impact of arrhythmia on the PPG signal may be based onthe arrhythmia type, magnitude, and frequency. For example, it may bepossible to determine respiration information from a PPG signal despiteRSA or occasional PVC, while AF or frequent PVC may interfere withdetermining respiration information from a PPG signal.

Morphology metrics may be parameters that may be calculated from the PPGsignal that provide information related to long term modulations orshort term modulations of the PPG signal based on physiologicalphenomena such as respiration or arrhythmia. Examples include a downmetric for a pulse, kurtosis for a pulse, the delta of the secondderivative (“DSD”) between consecutive pulses, the up metric for apulse, skew, b/a ratio, c/a ratio, peak amplitude of a pulse, center ofgravity of a pulse, or area of a pulse. Other information that may bedetermined by pre-processor 312 may include the pulse rate, thevariability of the period of the PPG signal, the variability of theamplitude of the PPG signal, and an age measurement indicative of theage of the useful portion of the analyzed PPG signal.

In some embodiments, pre-processor 312 may be coupled to processor 314and post-processor 316. Processor 314 and post-processor 316 may beimplemented by any suitable combination of hardware and software.Processor 314 may receive any suitable physiological information andcalculated parameters from pre-processor 312. For example, processor mayreceive a pulse rate value and morphology metrics for use in determiningrespiration information. Processor 314 may be coupled to post-processor316 and may communicate any suitable information such as respirationinformation to post-processor 316. Pre-processor 312 may also provideany suitable information to post-processor 316. Post-processor 316 mayutilize the received information to calculate and output any suitablephysiological parameter such as respiration information. Post-processor316 may provide the output information to output 318.

Processor 314, post-processor 316, or both, may determine any suitablephysiological parameter. In an exemplary embodiment, processor 314,post-processor 316, or both may determine respiration information suchas respiration rate. Respiration information such as respiration ratemay be determined in any suitable manner. In an exemplary embodiment, aplurality of morphology metric signals may be generated based on themorphology metrics, as is described in more detail in co-pending,commonly assigned U.S. patent application Ser. No. 13/243,853, filedSep. 23, 2011 and entitled “SYSTEMS AND METHODS FOR DETERMININGRESPIRATION INFORMATION FROM A PHOTOPLETHYSMOGRAPH,” which isincorporated by reference herein in its entirety. Respirationinformation may be determined based on the morphology metric signals inany suitable manner. In an exemplary embodiment, a correspondingautocorrelation sequence may be generated for each of the morphologymetric signals, and respiration information may be determined based onthe autocorrelation sequences. Autocorrelation sequences for determiningrespiration information may be generated in any suitable manner, such asis described in more detail in co-pending, commonly assigned U.S. patentapplication Ser. No. 13/243,951, filed Sep. 23, 2011 and entitled“SYSTEMS AND METHODS FOR DETERMINING RESPIRATION INFORMATION FROM APHOTOPLETHYSMOGRAPH,” which is incorporated by reference herein in itsentirety. Respiration information may then be determined fromautocorrelation signals in any suitable manner. In an exemplaryembodiment, respiration information may be determined directly from acombined autocorrelation sequence as is described in more detail inco-pending, commonly assigned U.S. patent application Ser. No.13/243,785, filed Sep. 23, 2011 and entitled “SYSTEMS AND METHODS FORDETERMINING RESPIRATION INFORMATION FROM A PHOTOPLETHYSMOGRAPH,” whichis incorporated by reference herein in its entirety. In anotherexemplary embodiment, respiration information may be determined based ona continuous wavelet transform as is described in more detail inco-pending, commonly assigned U.S. patent application Ser. No.13/243,892, filed Sep. 23, 2011 and entitled “SYSTEMS AND METHODS FORDETERMINING RESPIRATION INFORMATION FROM A PHOTOPLETHYSMOGRAPH,” whichis incorporated by reference herein in its entirety.

Output 318 may be any suitable output device such as one or more medicaldevices (e.g., a medical monitor that displays various physiologicalparameters, a medical alarm, or any other suitable medical device thateither displays physiological parameters or uses the output ofpost-processor 316 as an input), one or more display devices (e.g.,monitor, PDA, mobile phone, any other suitable display device, or anycombination thereof), one or more audio devices, one or more memorydevices (e.g., hard disk drive, flash memory, RAM, optical disk, anyother suitable memory device, or any combination thereof), one or moreprinting devices, any other suitable output device, or any combinationthereof.

In some embodiments, all or some of pre-processor 312, processor 314,and/or post-processor 316 may be referred to collectively as processingequipment. For example, processing equipment may be configured toamplify, filter, sample and digitize an input signal 310 and calculatephysiological information from the signal.

Pre-processor 312, processor 314, and post-processor 316 may be coupledto one or more memory devices (not shown) or incorporate one or morememory devices such as any suitable volatile memory device (e.g., RAM,registers, etc.), non-volatile memory device (e.g., ROM, EPROM, magneticstorage device, optical storage device, flash memory, etc.), or both.The memory may be used by pre-processor 312, processor 314, andpost-processor 316 to, for example, store data relating to input PPGsignals, morphology metrics, respiration information, arrhythmiafeatures, arrhythmia indicators, confidence values, or other informationcorresponding to physiological monitoring.

It will be understood that system 300 may be incorporated into system 10(FIGS. 1 and 2) in which, for example, input signal 310 may be generatedby sensor unit 12 (FIGS. 1 and 2), monitor 14 (FIGS. 1 and 2), or both.Pre-processor 312, processor 314, and post-processor 316 may each belocated in one of monitor 14 or display monitor 26 (or other devices),and may be split among multiple devices such as monitor 14 or displaymonitor 26. In some embodiments, portions of system 300 may beconfigured to be portable. For example, all or part of system 300 may beembedded in a small, compact object carried with or attached to thepatient (e.g., a watch, other piece of jewelry, or a smart phone). Insome embodiments, a wireless transceiver (not shown) may also beincluded in system 300 to enable wireless communication with othercomponents of system 10 (FIGS. 1 and 2). As such, system 10 (FIGS. 1 and2) may be part of a fully portable and continuous patient monitoringsolution. In some embodiments, a wireless transceiver (not shown) mayalso be included in system 300 to enable wireless communication withother components of system 10. For example, communications between oneor more of pre-processor 312, processor 314, and post-processor 316 maybe over BLUETOOTH, 802.11, WiFi, WiMax, cable, satellite, infrared, orany other suitable transmission scheme. In some embodiments, a wirelesstransmission scheme may be used between any communicating components ofsystem 300.

Respiratory activities may cause particular changes in the morphology ofa PPG signal throughout a respiratory cycle, including, for example, ona pulse by pulse basis. In some circumstances, these changes inmorphology may be in addition to morphological change due to arrhythmia,changes in stroke volume, pulse rate, blood pressure, any other suitablephysiological parameters, or any combination thereof. Respiratorymodulations may include baseline modulations, amplitude modulations,frequency modulations, respiratory sinus arrhythmia, any other suitablemodulations, or any combination thereof. Respiratory modulations mayexhibit different phases, amplitudes, or both, within a PPG signal andmay contribute to complex behavior (e.g., changes) of the PPG signal.Morphology metrics may be calculated on any portion of a PPG signal, butin one exemplary embodiment each consecutive set of fiducial points maydefine a relevant portion of the PPG signal for calculating a morphologymetric, and may be referred to herein as a fiducial-defined portion.Fiducial points for a PPG signal may be determined in any suitablemanner, such as is described in more detail in co-pending, commonlyassigned U.S. patent application Ser. No. 13/243,907, filed Sep. 23,2011 and entitled “SYSTEMS AND METHODS FOR DETERMINING RESPIRATIONINFORMATION FROM A PHOTOPLETHYSMOGRAPH,” which is incorporated byreference herein in its entirety.

An example of a PPG signal changing its morphology over a series ofpulse cycles associated with a respiratory cycle is depicted in FIG. 4and FIG. 5. A respiratory cycle may typically have a longer period(lower frequency) than a pulse cycle and may span a number of pulseperiods. A respiratory cycle may span a number of pulse cycles based onthe relative respiration rate and pulse rate. An exemplary respiratorycycle 402 may span four pulse periods as depicted in FIG. 4. Respirationmay impact the shape of the pulse waveform, e.g., by amplitude andfrequency modulation. For example, as depicted in FIG. 5, a first pulseassociated with the respiratory cycle may have a relatively lowamplitude as well as an obvious distinct dichrotic notch as indicated bypoint A. A second pulse may have a relatively high amplitude as well asa dichrotic notch that has been washed out as depicted by point B. FIG.5 depicts the pulses associated with point A and B superimposed on thesame scale for comparison. By the end of the respiratory cycle the pulsefeatures may again be similar to the morphology of A. Respiration mayhave varied effects on the morphology of a PPG signal other than thosedepicted in FIG. 5.

A PPG signal may also change its morphology when arrhythmia is present.In some instances it may be desirable to detect arrhythmia to assist indetermining physiological information based on morphology. For example,it may be difficult to distinguish morphology changes due to respirationfrom certain morphology changes due to arrhythmia, e.g., based on thetype of arrhythmia, the magnitude of the arrhythmia, and the frequencyof the arrhythmia. In some embodiments suitable processing may beperformed based on the detection of arrhythmia, e.g., to more accuratelycalculate a physiological parameter such as respiration information.

FIG. 6 depicts steps 600 for detecting arrhythmia from a physiologicalsignal such as a PPG signal in accordance with some embodiments of thepresent disclosure. Although an exemplary embodiment is describedherein, it will be understood that each of steps 600 may be performed bypre-processor 312, processor 314, post-processor 316, or any combinationthereof. It will also be understood that steps 600 may be performed inalternative sequence or in parallel, that steps may be omitted, and thatadditional steps may be added or inserted.

At step 602 pre-processor 312 may process a received signal such as aPPG signal. Processing the received signal may include any suitableprocessing steps useful to prepare the PPG signal to be analyzed forarrhythmia, such as establishing fiducial points, filtering the PPGsignal, and identifying any problematic portions of the PPG signal.Examples of problematic portions of the PPG signal may be portions (aswell as surrounding portions) that demonstrate a large baseline shift,the presence of motion artifacts, large pulse period variability, or anout of range pulse rate. Any problematic portions of the signal may bediscarded, filtered, zeroed out, or otherwise processed in any suitablemanner.

At step 604 pre-processor 312 may extract one or more derived values foran analysis window to generate one or more derived value data sets.Derived value data sets are described herein in terms of derived valuessignals for clarity and brevity. Extracted derived values may includeany suitable derived values for detecting arrhythmia from the receivedsignal, such as pulse rate, morphology metrics, a FM demodulated PPGsignal, any other suitable derived value, or any combination thereof.Each resulting derived value signal may consist of a sequence of samplesdetermined in any suitable manner, e.g., at a set sampling rate, basedon a pulse rate, or for each fiducial-defined portion of the analysiswindow. Any number of derived value signals may be determined from thePPG signal. Any suitable aspect of a FM demodulated signal may be usedas a derived value for detecting arrhythmia, such as the magnitude ofthe FM demodulated signal. Any suitable morphology metric may be used asa derived value for detecting arrhythmia, such as a down metric,kurtosis metric, DSD derivative metric, or b/a ratio metric.

The down metric is the difference between a first (e.g., fiducial)sample of a fiducial-defined portion of the PPG signal and a minimumsample of the fiducial-defined portion of the PPG signal. The DSD metricis the delta (difference) between fiducial points in consecutivefiducial-defined portions of the second derivative of the PPG signal.

The PPG signal may include a number of peaks (e.g., four peakscorresponding to maxima and minima) which may be described as thea-peak, b-peak, c-peak, and d-peak, with the a-peak and c-peak generallycorresponding to local maxima within a fiducial-defined portion and theb-peak and d-peak generally corresponding to local minima within afiducial-defined portion. For example, the PPG signal (or a signalderived from the PPG signal) may include four peaks: the a-peak, b-peak,c-peak, and d-peak. Each peak may be indicative of a respective systolicwave, i.e., the a-wave, b-wave, c-wave, and d-wave. The b/a ratio metricis based on the ratio between the b-peak and a-peak of the PPG signal(i.e., b/a), e.g., in the second derivative.

Kurtosis measures the peakedness of a signal, such as the PPG signal, afirst or second derivative of the PPG signal, or other derived valuesignals. In an exemplary embodiment a kurtosis metric may be based onthe first derivative of the PPG signal. The kurtosis of a signal may becalculated based on the following formulae:

$\begin{matrix}{D = {\frac{1}{n}{\sum\limits_{i = 1}^{n}\left( {x_{i}^{\prime} - {\overset{\_}{x}}^{\prime}} \right)^{2}}}} & (14) \\{{Kurtosis} = {\frac{1}{n\; D^{2}}{\sum\limits_{i = 1}^{n}\left( {x_{i}^{\prime} - {\overset{\_}{x}}^{\prime}} \right)^{4}}}} & (15)\end{matrix}$

where:x_(i)′=ith sample of 1^(st) derivative;x′=mean of 1st derivative of fiducial-defined portion;n=set of all samples in the fiducial-defined portion

At step 606 processor 314 may calculate one or more arrhythmia featuresbased on the one or more derived value signals received frompre-processor 312. The arrhythmia features may be the result of anysuitable calculations such as a standard deviation, entropy, kurtosis,and dot product. Any number of arrhythmia features may be calculatedfrom a single derived value signal and any combination of derived valuessignals may be utilized as inputs to determine a single arrhythmiafeature. Each arrhythmia feature may provide an indication of thepresence and/or type of arrhythmia, based on the underlying derivedvalue data set and the calculation performed. For example, the standarddeviation may be indicative of the variation of an underlying derivedvalue over an analysis window, entropy may indicate the randomness of anunderlying derived value over an analysis window, the dot product may beindicative of similarity or difference in the phase of the derivedvalues over an analysis window, and kurtosis may be sensitive to changesin both frequency and magnitude over an analysis window. Exemplaryarrhythmia features include the standard deviation of the pulse rate,the entropy of the pulse rate, the dot product of the kurtosis metricand the b/a ratio metric, the kurtosis of the pulse rate, the standarddeviation of the down metric, the entropy of the down metric, thekurtosis of the down metric, the standard deviation of the kurtosismetric, the entropy of the kurtosis metric, the kurtosis of the kurtosismetric, the standard deviation of the DSD metric, the entropy of the DSDmetric, the kurtosis of the DSD metric, the standard deviation of the FMdemodulated PPG signal, the entropy of the FM demodulated PPG signal,the kurtosis of the FM demodulated PPG signal, and any other suitablecalculation based on a derived value signal.

At step 608 processor 314 may detect arrhythmia based on the one or morearrhythmia features. Each arrhythmia feature may provide someinformation relating to the presence of arrhythmia based on theunderlying derived value or derived values used to generate the derivedvalue signal and the calculations used to generate the arrhythmiafeature. An arrhythmia indicator may be generated from the one or morearrhythmia features in any suitable manner. In an exemplary embodiment,a learning algorithm such as a perceptron algorithm may be used todetermine which combination of derived value signals and calculationmethods best detect arrhythmia or distinguish between arrhythmias basedon training data. A resulting arrhythmia classifier/detector may use anykind of linear or nonlinear classifiers based on input features. Theoutput of such a classifier/detector is used to generate an arrhythmiaresult used to determine an arrhythmia indicator. The resulting weights,bias value, and arrhythmia features may be inputs into a trained neuralnet. Fuzzy logic may also be used to implement the decision logic.

The arrhythmia indicator may be indicative of the presence of arrhythmia(e.g., an indicator of “arrhythmia” vs. “no arrhythmia”), of aclassification of arrhythmia (e.g., an indicator of “good arrhythmia”vs. “bad arrhythmia”), or a type of arrhythmia (e.g., an indicator ofRSA, PVC, or AF). The arrhythmia indicator may be in any form, such as abinary value or a range indicating a confidence level. For example, anexemplary arrhythmia indicator may be a confidence value based on a softthresholding logic, with arrhythmia result values exceeding a firstthreshold resulting in a “1” (indicative of no arrhythmia), arrhythmiaresult values falling below a second threshold resulting in a “0”(indicative of arrhythmia), and arrhythmia result values falling betweenthe first and second threshold having a value indicative of thelikelihood of arrhythmia.

At step 610 processor 314 may respond based on the arrhythmia indicator.Exemplary responses may be to display or communicate the presence ofarrhythmia, display or communicate a type of arrhythmia, modify thecalculation of a physiological parameter based on the arrhythmiaindicator, change a weighting factor for the most recent value of aphysiological parameter based on the arrhythmia indicator, blockprocessor 314 from calculating a physiological parameter when arrhythmiais present, block the display of a physiological parameter whenarrhythmia is present, or perform any other suitable operation based onthe arrhythmia indicator.

FIGS. 7A and 7B depict steps 700 for detecting arrhythmia from a PPGsignal in accordance with some embodiments of the present disclosure. Itwill be understood that steps described herein are exemplary, and thatsome or all of the steps herein may be omitted, additional steps may beadded, and the order of the steps may be modified in any suitablemanner. It will further be understood that the particular derived valuesignals, arrhythmia features, arrhythmia indicators, and the stepsdescribed herein are exemplary only and may be modified in any suitablemanner, e.g., as described herein with respect to FIG. 6. It will beunderstood that the each of the steps herein may be performed bypre-processor 312, processor 314, post-processor 316, or any combinationthereof.

An input signal may be a PPG signal encompassing an analysis window,e.g., a 45 second analysis window. At step 702, pre-processor 312 mayextract any suitable derived value signals from the PPG signal, such asa pulse rate signal, a kurtosis metric signal, and a b/a ratio metricsignal. The pulse rate signal may be a sequence of samples indicatingthe change in the pulse rate over the analysis period, e.g., based onthe pulse period. The kurtosis metric signal and b/a ratio metric signalmay each be a sequence of samples wherein each respective metric valueis calculated for each fiducial-defined portion within the analysiswindow.

At step 704 pre-processor 312 may determine whether to exclude anyportions of the PPG signal from analysis by the arrhythmia detector. Inan exemplary embodiment, pre-processor 312 may analyze a baseline shiftof the PPG signal. A large baseline shift may be indicative of a motionartifact or other artificial change in the signal baseline. A baselineshift may be detected in any suitable manner, such as is described inmore detail in co-pending, commonly assigned U.S. patent applicationSer. No. 13/243,853, filed Sep. 23, 2011 and entitled “SYSTEMS ANDMETHODS FOR DETERMINING RESPIRATION INFORMATION FROM APHOTOPLETHYSMOGRAPH,” which was incorporated by reference herein in itsentirety above. At step 706 pre-processor 312 may determine the usableportion of the derived value signals based on the baseline shift outputof step 704. The usable portion may be determined in any suitablemanner, such as excluding any portion of the derived value signals thatoccur prior to the occurrence of the baseline shift within the analysiswindow. The resulting derived value signals, adjusted for any largebaseline shift, may be output to processor 314.

Arrhythmia features may be calculated from the derived value signals atsteps 708, 710, and 712. It will be understood that one or morearrhythmia features may be determined in any suitable manner, and thatany number of derived values may be used to calculate arrhythmiafeatures. In an exemplary embodiment processor 314 may determine astandard deviation arrhythmia feature, an entropy arrhythmia feature,and a dot product arrhythmia feature. At step 708 processor 314 maydetermine a standard deviation arrhythmia feature based on the pulserate derived value signal. For a sequence of N samples of the pulse ratederived value signal, a standard deviation arrhythmia feature may becalculated as follows:

$\begin{matrix}{{std} = \sqrt{\frac{1}{N - 1}{\sum\limits_{i = 1}^{N}\left( {x_{i} - \overset{\_}{x}} \right)^{2}}}} & (16)\end{matrix}$

where:std=standard deviation;N=number of samples;x_(i)=pulse rate for sample i; andx=mean pulse rate for the analysis window.

At step 710 processor 314 may determine an entropy arrhythmia featurebased on the pulse rate derived value signal. A set of M evenly spacedbins may be allocated to cover a given pulse rate range, and each pulserate sample may be assigned to a corresponding bin. In an exampleembodiment, 20 evenly spaced bins may be allocated to a pulse rate rangeof 20-250 beats per minute. A probability distribution may be calculatedfor each of the M bins as follows:

$\begin{matrix}{P_{i} = \frac{\# {\_ of}{\_ pulses}}{N}} & (17)\end{matrix}$

where:P_(i)=probability distribution for bin i;N=total number of samples in sampling window; and#_of_pulses=number of pulses with a pulse rate in the ith bin.

The entropy arrhythmia feature may be calculated from the probabilitydistribution as follows:

$\begin{matrix}{e = {- {\sum\limits_{i = 1}^{M}{P_{i}\log \; P_{i}}}}} & (18)\end{matrix}$

where:e=entropy arrhythmia feature; andM=number of bins.

At step 712 processor 314 may determine a dot product arrhythmia featurebased on the kurtosis derived value signal and the b/a ratio derivedvalue signal. The kurtosis derived value and b/a ratio derived value mayshow a relatively strong correlation for a normal physiological signal,but a weaker correlation when an arrhythmia such as PVC is present. Thedot product arrhythmia feature may be calculated as follows:

$\begin{matrix}{{D\; P} = {\frac{1}{N}{\sum\limits_{i = 1}^{N}\left( {{kur}_{i} \cdot {baRatio}_{i}} \right)}}} & (19)\end{matrix}$

where:DP=dot product arrhythmia feature;N=number of samples;kur_(i)=kurtosis value for sample i; andbaRatio_(i)=b/a ratio value for sample i.

Referring to FIG. 7B, the calculated arrhythmia features may be used todetermine an arrhythmia indicator. In an exemplary embodiment, thearrhythmia features may be inputs to a linear classifier or other typeof linear or nonlinear classifiers such as neural networks fordetermining the arrhythmia indicator. At step 714 the standard deviationarrhythmia feature may be multiplied by weight w₁ (i.e., w_(std)), atstep 716 the entropy arrhythmia feature may be multiplied by weight w₂(i.e., w_(e)), and at step 718 the dot product arrhythmia feature may bemultiplied by weight w₃ (i.e., w_(DP)). Each of weights w₁ w₂, and w₃may be determined in any suitable manner, such as based on a learningalgorithm. At step 720 the weighted arrhythmia features may be combinedwith a bias value b₀, which may be determined in any suitable mannersuch as a based on a learning algorithm. At step 722, a distance to thedecision plane (decision value) d may be calculated from the output ofstep 720 based on the following:

$\begin{matrix}{d = \frac{{w_{std}x_{std}} + {w_{e}x_{e}} + {w_{D\; P}x_{D\; P}} + b_{0}}{W}} & (20)\end{matrix}$

where:d=distance to decision plane;W_(std)=standard deviation weight;X_(std)=standard deviation arrhythmia feature;W_(e)=entropy weight;X_(std)=entropy arrhythmia feature;W_(DP)=dot product weight;X_(DP)=dot product arrhythmia feature;b₀=bias value; and∥W∥=norm of the vector W=[W_(std) W_(e) W_(DP)]^(T).

At step 724 the decision value d may be compared to a threshold distanced₀ based on soft thresholding logic. In an exemplary embodiment, ifd≧d₀, an arrhythmia indicator 726 of “1” may be indicative of noarrhythmia, or in some embodiments, of a good arrhythmia that is notexpected to interfere with the calculation of a physiological parametersuch as respiration rate. If d≦−d₀, an arrhythmia indicator 730 of “0”may be indicative of arrhythmia, or in some embodiments, of a badarrhythmia that may interfere with the calculation of a physiologicalparameter such as respiration rate. If −d₀<d<d₀, an arrhythmia indicator728 may be indicative of a possible or mild arrhythmia. At step 732 aconfidence value may be determined. The confidence value may correspondto the arrhythmia indicator if the arrhythmia indicator is 1 or 0. If−d₀<d<d₀, a confidence value (e.g., between 1 and 0) may be calculatedat step 732 using soft thresholding as follows:

$\begin{matrix}{{f(d)} = {\frac{d}{2\; d_{0}} + {1/2}}} & (21)\end{matrix}$

where:f(d)=confidence value;d=distance to decision plane; andd₀=threshold distance.

The resulting confidence value of 1, 0, or f(d) may be utilized byprocessor 314 or post-processor 316 for the processing of physiologicalinformation such as respiration information. Exemplary responses may beto display or communicate the presence of arrhythmia, display orcommunicate a type of arrhythmia, modify the calculation of thephysiological parameter based on the arrhythmia indicator, change aweighting factor for the most recent value of the physiologicalparameter based on the arrhythmia indicator, block processor 314 fromcalculating the physiological parameter when arrhythmia is present,block the display of the physiological parameter when arrhythmia ispresent, or perform any other suitable operation based on the arrhythmiaindicator. In an exemplary embodiment a current value of thephysiological information that corresponds to the current analysiswindow may not be displayed if the confidence value is 0, while aconfidence value between 0 and 1 (i.e., f(d)) may result in the currentvalue being averaged with historical values based on a weighting factorthat corresponds to the confidence value.

FIG. 8 depicts steps for using frequency demodulation to determine aderived value signal in accordance with some embodiments of the presentdisclosure. The steps depicted in FIG. 8 may be performed bypre-processor 312, processor 314, post-processor 316, or any combinationthereof. It will also be understood that steps 800 may be performed inalternative sequence or in parallel, that steps may be omitted, and thatadditional steps may be added or inserted.

At step 802 pre-processor 312 may receive a PPG signal spanning ananalysis window as well as a calculated pulse rate for the analysiswindow. An exemplary PPG signal is depicted in FIG. 9 as PPG signal 902.At step 804 the PPG signal may be band pass filtered based on the pulserate to remove frequency information outside of a window of interest. Inan exemplary embodiment a band pass filter may be from 0.1 to 1.9 timesthe pulse rate. At step 806 the resulting band pass filtered PPG signalmay be FM demodulated in any suitable manner, e.g., to resolve aliasingissues in the underlying PPG signal. At step 808 the resulting FMdemodulated signal may be band pass filtered about a region of interestfor respiration, e.g., within a frequency window corresponding to 3-40breaths per minute. An exemplary resulting signal is depicted in FIG. 9as signal 904. At step 810 the resulting signal may be subsampled basedon the pulse rate, as is depicted by sampling points 906 of FIG. 9. Atstep 812 the samples may be output as a FM demodulated derived valuesignal for use in determining one or more arrhythmia features asdescribed herein, e.g., by determining the entropy or standard deviationof the FM demodulated derived value signal.

The foregoing is merely illustrative of the principles of thisdisclosure and various modifications may be made by those skilled in theart without departing from the scope of this disclosure. The abovedescribed embodiments are presented for purposes of illustration and notof limitation. The present disclosure also can take many forms otherthan those explicitly described herein. Accordingly, it is emphasizedthat this disclosure is not limited to the explicitly disclosed methods,systems, and apparatuses, but is intended to include variations to andmodifications thereof, which are within the spirit of the followingclaims.

What is claimed is:
 1. A physiological monitoring system for detectingarrhythmia in a subject, the system comprising: an interface configuredto receive a plethysmograph signal; and processing equipment configuredto: filter the plethysmograph signal based on a pulse rate of thesubject to generate a filtered signal; perform an FM demodulation of thefiltered signal to generate a demodulated signal; filter the demodulatedsignal based on respiration to generate a filtered demodulated signal;and subsample the filtered demodulated signal to generate a demodulatedderived value signal; determine a plurality of arrhythmia features fromthe demodulated derived value signal; and generate an arrhythmiaindicator based on the arrhythmia features.
 2. The system of claim 1,wherein the plurality of arrhythmia features comprise at least one of anentropy feature and a standard deviation feature.
 3. The system of claim1, wherein the processor is further configured to determine respirationinformation based on the arrhythmia indicator.
 4. The system of claim 1,wherein the filtering the plethysmograph signal based on the pulse rateof the subject comprises band pass filtering the plethysmograph signalwith a band pass range determined based on the pulse rate of thesubject.
 5. The system of claim 4, wherein the band pass range is set at0.1 to 1.9 times the pulse rate of the subject.
 6. The system of claim1, wherein the filtering the demodulated signal based on respirationcomprises band pass filtering the demodulated signal with a frequencywindow corresponding to 3 to 40 breaths per minute.
 7. The system ofclaim 1, wherein the subsampling the filtered demodulated signalcomprises subsampling the filtered demodulated signal based on the pulserate of the subject.
 8. The system of claim 1, wherein the plurality ofarrhythmia features comprise a dot product feature.
 9. The system ofclaim 1, wherein generating the arrhythmia indicator comprises:calculating a distance value based on the plurality of arrhythmiafeatures; comparing the distance value to a threshold distance value;and generating the arrhythmia indicator value based on the distancevalue and the threshold distance value.
 10. A method for detectingarrhythmia from a plethysmograph signal, the method comprising:extracting, using processing equipment, a plurality of derived valuedata sets associated with the plethysmograph signal; determining, usingprocessing equipment, a plurality of arrhythmia features based on thederived value data sets, wherein the arrhythmia features comprise anentropy feature; and generating, using processing equipment, anarrhythmia indicator based on the arrhythmia features.
 11. The method ofclaim 10, wherein the derived value data sets comprise at least one of apulse rate data set, a kurtosis data set, and a b/a ratio data set. 12.The method of claim 10, wherein the arrhythmia features further comprisea standard deviation feature.
 13. The method of claim 10, wherein thearrhythmia features further comprise a dot product feature.
 14. Themethod of claim 10, wherein generating the arrhythmia indicatorcomprises: calculating a distance value based on the plurality ofarrhythmia features; comparing the distance value to a thresholddistance value; and generating the arrhythmia indicator based on thedistance value and the threshold distance value.
 15. The method of claim14, wherein comparing the distance value comprises determining whetherthe distance value falls within a range of values based on the thresholddistance value.
 16. The method of claim 15, wherein the range of valuesis less than a positive threshold distance value and greater than anegative threshold distance value.
 17. The method of claim 14, whereincalculating the distance value comprises: modifying each of theplurality of arrhythmia features based on one or more weighting factors;and calculating the distance value based on the modified arrhythmiafeatures and a bias term.
 18. The method of claim 10, wherein generatingthe arrhythmia indicator comprises determining a confidence value. 19.The method of claim 10, further comprising determining respirationinformation based on the arrhythmia indicator.
 20. A patient monitoringsystem comprising: an interface configured to receive a plethysmographsignal; and a processor configured to: extract a plurality of derivedvalue data sets associated with the plethysmograph signal; determine aplurality of arrhythmia features based on the derived value data sets,wherein the arrhythmia features comprise an entropy feature; andgenerate an arrhythmia indicator based on the arrhythmia features.